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    "# REINFORCE Policy Optimization with TensorFlow\n",
    "\n",
    "In this notebook, we will look at policy optimization using TensorFlow. Let us quickly go through the derivation of what is policy Optimization and then we will apply this to CartPole environment - just like what we did in Chapter 6. \n",
    "\n",
    "\n",
    "### Derivation of Policy Gradient\n",
    "In Policy Optimization, we will have a neural network which takes in state `s` as input and produces the `logits` for action probabilities. \n",
    "\n",
    "The policy is parameterized by $\\theta$\n",
    "$$\\pi_\\theta(a|s)$$\n",
    "\n",
    "The agent follows the policy and generates the trajectory $\\large \\tau$ \n",
    "\n",
    "$$ s_1 \\rightarrow a_1 \\rightarrow s_2 \\rightarrow a_2 \\rightarrow .... \\rightarrow s_{T-1} \\rightarrow a_{T-1} \\rightarrow s_T \\rightarrow a_T$$ \n",
    "\n",
    "here $s_T$ is not necessarily the terminal state but some time horizon T upto which we are looking at the trajectory. \n",
    "\n",
    "The probability of trajectory $\\large \\tau$ depends on the transition probabilities $p(s_t+1 | s_t, a_t)$ and the policy $\\pi_\\theta(a_t|s_t)$. It is given by the expression:\n",
    "\n",
    "$$p_\\theta(\\tau) = p_\\theta(s_1, a_1, s_2, a_2, ..., s_T, a_T) = p(s_1)\\prod_{t=1}^{T}\\pi_\\theta(a_t|s_t)p(s_{t+1}|s_t,a_t)$$\n",
    "\n",
    "The expected return from following the policy $\\pi$ is given by:\n",
    "\n",
    "$$J(\\theta) = {\\large E}_{\\tau \\sim p_\\theta(\\tau)} \\left[ \\sum_{t} \\gamma^t r(s_t, a_t) \\right]$$\n",
    "\n",
    "We want to find the $\\theta$ which maximizes the expected reward/return $J(\\theta)$. In other words, the optimal $\\theta=\\theta^*$ is given by expression\n",
    "\n",
    "$$\\theta^* = arg \\underset{\\theta}{max}{\\large E}_{\\tau \\sim p_\\theta(\\tau)} \\left[ \\sum_{t} \\gamma^t r(s_t, a_t) \\right] $$\n",
    "\n",
    "Moving on, let us try to find the optimal $\\theta$. To keep the notations easier to understand, we will replace $\\sum_{t} \\gamma^t r(s_t, a_t)$ as $r(\\tau)$:\n",
    "\n",
    "$$J(\\theta) = {\\large E}_{\\tau \\sim p_\\theta(\\tau)} \\left[ r(\\tau) \\right] = \\int p_\\theta(\\tau)r(\\tau) d\\tau$$\n",
    "\n",
    "We take the gradient/derivative of above expression with respect to $\\theta$:\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  \\nabla_{\\theta} \\int p_\\theta(\\tau)r(\\tau) d\\tau $$\n",
    "\n",
    "By linearity we can move the gradient inside the integral:\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  \\int \\nabla_{\\theta} p_\\theta(\\tau)r(\\tau) d\\tau $$\n",
    "\n",
    "Using log derivative trick, we know that $\\nabla_x f(x) = f(x) \\nabla_x \\log{f(x)}$. Using this we can write above expression as:\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  \\int p_\\theta(\\tau) \\left[ \\nabla_{\\theta}\\log{p_\\theta(\\tau)} r(\\tau) \\right] d\\tau $$\n",
    "\n",
    "We can now write the integral back as expectation, which gives us the expression:\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  {\\large E}_{\\tau \\sim p_\\theta(\\tau)} \\left[ \\nabla_{\\theta}\\log{p_\\theta(\\tau)} r(\\tau) \\right] $$\n",
    "\n",
    "Let us now expand the term $\\nabla_{\\theta}\\log{p_\\theta(\\tau)}$ by writing out the full expression of $p_\\theta(\\tau)$. \n",
    "\n",
    "$$ \\nabla_{\\theta}\\log{p_\\theta(\\tau)}  = \\nabla_{\\theta} \\log{ \\left[ p(s_1) \\prod_{t=1}^{T}\\pi_\\theta(a_t|s_t)p(s_{t+1}|s_t,a_t)\\right]}$$\n",
    "\n",
    "We know that log of product of terms can be written as sum of log of terms i.e. \n",
    "\n",
    "$$\\log{\\prod_i f_i(x)} = \\sum_i log{f_i(x)}$$ \n",
    "\n",
    "Using the above substitution, we get:\n",
    "\n",
    "$$ \\nabla_{\\theta}\\log{p_\\theta(\\tau)}  = \\nabla_{\\theta} \\left[ log{p(s_1)} +  \\sum_{t=1}^{T} \\left\\{ \\log{ \\pi_\\theta(a_t|s_t)} + \\log{p(s_{t+1}|s_t,a_t)} \\right\\} \\right]$$\n",
    "\n",
    "The only term dependent on $\\theta$ is $\\pi_\\theta(a_t|s_t)$. The other two terms $log{p(s_1)}$ and $\\log{p(s_{t+1}|s_t,a_t)}$ do not depend on $\\theta$. Accordingly, we can simplify the above expression as:\n",
    "\n",
    "$$ \\nabla_{\\theta}\\log{p_\\theta(\\tau)}  = \\sum_{t=1}^{T} \\nabla_{\\theta} \\log{ \\pi_\\theta(a_t|s_t)} $$\n",
    "\n",
    "\n",
    "Substituting the above term into the expression for $\\nabla_{\\theta} J(\\theta)$, as well as expanding $r(\\tau)$ we get:\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  {\\large E}_{\\tau \\sim p_\\theta(\\tau)} \\left[ \\left( \\sum_{t=1}^{T} \\nabla_{\\theta} \\log{ \\pi_\\theta(a_t|s_t)} \\right) \\left( \\sum_{t=1}^{T} \\gamma^t r(s_t, a_t) \\right) \\right] $$\n",
    "\n",
    "We can now replace the outer expectation with an estimate over multiple trajectories to get the following expression for the gradient of policy objective:\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  \\frac{1}{N} \\sum_{i=1}^{N} \\left[ \\left( \\sum_{t=1}^{T} \\nabla_{\\theta} \\log{ \\pi_\\theta(a_t^i|s_t^i)} \\right) \\left( \\sum_{t=1}^{T} \\gamma^t r(s_t^i, a_t^i) \\right) \\right] $$\n",
    "\n",
    "where i denotes the $i^{th}$ trajectory. \n",
    "\n",
    "To improve the policy, we now take a +ve step in $\\theta$ in the direction of $\\nabla_{\\theta} J(\\theta)$:\n",
    "\n",
    "$$\\theta = \\theta + \\alpha \\nabla_{\\theta} J(\\theta)$$\n",
    "\n",
    "To summarize, we design a model which takes state $s$ as input and produces the policy distribution $\\pi_\\theta(a|s)$ as the output of the model. We use a policy to generate returns and then change the model parameter $\\theta$ using the expression: $\\theta = \\theta + \\alpha \\nabla_{\\theta} J(\\theta)$\n",
    "\n",
    "\n",
    "### Rewards to Go Trick\n",
    "\n",
    "\n",
    "we drop the reward terms that came before time t as at time t, the action we take can only impact the reward which comes at time t and later. This leads to changing the 2nd inner sum going from t’=t to T instead of earlier sum over t’ going from t’=1 to T. i.e. the start index is now t’=t and not t=1. The revised expression is given below:\n",
    "\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  \\frac{1}{N} \\sum_{i=1}^{N} \\left[  \\sum_{t=1}^{T}  \\left( \\nabla_{\\theta} \\log{ \\pi_\\theta(a_t^i|s_t^i)} \\sum_{t'=t}^{T} \\gamma^{t'-t} r(s_{t'}^i, a_{t'}^i) \\right) \\right] $$\n",
    "\n",
    "\n",
    "### Implementing Loss and Gradient Step in PyTorch/TensorFlow\n",
    "\n",
    "We will implement a pseudo loss function, whose derivative will give us $\\nabla_{\\theta} J(\\theta)$. Also as PyTorch/TensorFlow carryout a gradient Step, we will convert maximization to minimization by changing the sign of this objective function\n",
    "\n",
    "$$L_{CrossEntropy}(\\theta) = - J(\\theta) = - \\frac{1}{N} \\sum_{i=1}^{N} \\left[ \\sum_{t=1}^{T} \\left(  \\log{ \\pi_\\theta(a_t^i|s_t^i)} \\sum_{t'=t}^{T} \\gamma^{t'-t} r(s_{t'}^i, a_{t'}^i) \\right) \\right] $$\n",
    "\n",
    "To summarize, we will pass the state `s` through the network to get $\\log{ \\pi_\\theta(a_t^i|s_t^i)}$. We will calculate the cross_entropy loss for the actions actually seen in the trajectory. We will then calculate the weighted mean of these individual loss terms in the trajectory with weights being the rewards-to-go $\\sum_{t'=t}^{T} \\gamma^{t'-t} r(s_{t'}^i, a_{t'}^i)$\n",
    "\n",
    "This will be followed by a gradient step in -ve direction of weighted NLL (negative log loss) i.e. in positive direction of the gradient of $J(\\theta)= - L_{CrossEntropy}(\\theta)$ \n",
    "\n",
    "We also add a regularization term known as Entropy. Entropy of a distribution is defined as:\n",
    "\n",
    "$$H(X) = \\sum_x -p(x).log(p(x))$$\n",
    "\n",
    "To keep enough exploration, we will want the probability to have a spread out distribution and not let the probability distribution to collapse to a single value or a small region too soon. BIgger the spread of a distribution, higher the entropy H(x) of a distribution. Accordingly, the term fed into PyTorch/TensorFlow minimizer is:\n",
    "\n",
    "\n",
    "$$Loss(\\theta) = - J(\\theta) - H(\\pi_\\theta(a_t^i|s_t^i)) = - \\frac{1}{N} \\sum_{i=1}^{N} \\left[ \\sum_{t=1}^{T} \\left(  \\log{ \\pi_\\theta(a_t^i|s_t^i)} \\sum_{t'=t}^{T} \\gamma^{t'-t} r(s_{t'}^i, a_{t'}^i) \\right) - \\beta \\sum_{a_i} \\pi_\\theta(a_t^i|s_t^i).\\log{ \\pi_\\theta(a_t^i|s_t^i)} \\right] $$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense, Flatten\n",
    "from tensorflow.keras import Model\n",
    "import gym\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import convolve, gaussian\n",
    "\n",
    "import os\n",
    "import io\n",
    "import base64\n",
    "import time\n",
    "import glob\n",
    "from IPython.display import HTML\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Environment - CartPole \n",
    "\n",
    "We can use the setup here to run on any environment which has state as a single vector and actions are discrete. We will build it on Cart Pole and they try to run this on many other environments like Atari games and others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_env(env_name, seed=None):\n",
    "    # remove time limit wrapper from environment\n",
    "    env = gym.make(env_name).unwrapped\n",
    "    if seed is not None:\n",
    "        env.seed(seed)\n",
    "    return env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "env_name = 'CartPole-v1'\n",
    "\n",
    "env = make_env(env_name)\n",
    "env.reset()\n",
    "plt.imshow(env.render(\"rgb_array\"))\n",
    "state_shape, n_actions = env.observation_space.shape, env.action_space.n\n",
    "state_dim = state_shape[0]\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Policy Network\n",
    "\n",
    "We will build a simple network that takes in state and produces logits for the action probabilities. We will keep network simple. he observation space and action space is as given below for CartPole\n",
    "\n",
    "    Observation:\n",
    "        Type: Box(4)\n",
    "        Num     Observation               Min                     Max\n",
    "        0       Cart Position             -4.8                    4.8\n",
    "        1       Cart Velocity             -Inf                    Inf\n",
    "        2       Pole Angle                -0.418 rad (-24 deg)    0.418 rad (24 deg)\n",
    "        3       Pole Angular Velocity     -Inf                    Inf\n",
    "    Actions:\n",
    "        Type: Discrete(2)\n",
    "        Num   Action\n",
    "        0     Push cart to the left\n",
    "        1     Push cart to the right\n",
    "        \n",
    "\n",
    "The model will be a simple one with 1 hidden layer with Relu activation and final layer being logits with dimension equal to number of actions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(128, activation='relu', input_shape=(state_dim,)),\n",
    "    tf.keras.layers.Dense(n_actions)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 128)               640       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 898\n",
      "Trainable params: 898\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict Action Probabilities\n",
    "\n",
    "We will use this function to generate the trajectory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_probs(states):\n",
    "    \"\"\"\n",
    "    params: states: [batch, state_dim]\n",
    "    returns: probs: [batch, n_actions]\n",
    "    \"\"\"\n",
    "    logits = model(states)\n",
    "    probs = tf.nn.softmax(logits, axis=-1).numpy()\n",
    "    return probs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Play game and generate Trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_trajectory(env, n_steps=1000):\n",
    "    \"\"\"\n",
    "    Play a session and genrate a trajectory\n",
    "    returns: arrays of states, actions, rewards\n",
    "    \"\"\"\n",
    "    states, actions, rewards = [], [], []\n",
    "    \n",
    "    # initialize the environment\n",
    "    s = env.reset()\n",
    "    \n",
    "    #generate n_steps of trajectory:\n",
    "    for t in range(n_steps):\n",
    "        action_probs = predict_probs(np.array([s]))[0]\n",
    "        #sample action based on action_probs\n",
    "        a = np.random.choice(n_actions, p=action_probs)\n",
    "        next_state, r, done, _ = env.step(a)\n",
    "        \n",
    "        #update arrays\n",
    "        states.append(s)\n",
    "        actions.append(a)\n",
    "        rewards.append(r)\n",
    "        \n",
    "        s = next_state\n",
    "        if done:\n",
    "            break\n",
    "    \n",
    "    return states, actions, rewards"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Rewards to Go\n",
    "\n",
    " $G(s_t) = \\sum_{t'=t}^{T} \\gamma^{t-t'} r(s_{t'}^i, a_{t'}^i)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_rewards_to_go(rewards, gamma=0.99):\n",
    "    \n",
    "    T = len(rewards) # total number of individual rewards\n",
    "    # empty array to return the rewards to go\n",
    "    rewards_to_go = [0]*T \n",
    "    rewards_to_go[T-1] = rewards[T-1]\n",
    "    \n",
    "    for i in range(T-2, -1, -1): #go from T-2 to 0\n",
    "        rewards_to_go[i] = gamma * rewards_to_go[i+1] + rewards[i]\n",
    "    \n",
    "    return rewards_to_go"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train on one trajectory\n",
    "\n",
    "We will calculate the loss and take a gradient step. We will use Adam Optimizer\n",
    "\n",
    "We are taking only one trajectory. so N=1. We will however, average it over the number of actions to get the average loss. So the function we will actually implement is as given below:\n",
    "\n",
    "$$Loss(\\theta) = - J(\\theta) - H(\\pi_\\theta(a_t|s_t)) = - \\frac{1}{T}  \\sum_{t=1}^{T} \\left( \\log{ \\pi_\\theta(a_t|s_t)} G(s_t) - \\beta \\sum_{a_i} \\pi_\\theta(a_t|s_t).\\log{ \\pi_\\theta(a_t|s_t)} \\right) $$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#init Optimizer\n",
    "optimizer = tf.keras.optimizers.Adam()\n",
    "\n",
    "def train_one_episode(states, actions, rewards, gamma=0.99, entropy_coef=1e-2):\n",
    "    \n",
    "    \n",
    "    # get rewards to go\n",
    "    rewards_to_go = get_rewards_to_go(rewards, gamma)\n",
    "    \n",
    "    # convert to numpy arrays\n",
    "    states = np.array(states)\n",
    "    actions = np.array(actions)\n",
    "    rewards_to_go = np.array(rewards_to_go)\n",
    "\n",
    "    with tf.GradientTape() as tape:\n",
    "\n",
    "        # get action probabilities from states\n",
    "        logits = model(states)\n",
    "        probs = tf.nn.softmax(logits, -1)\n",
    "        log_probs = tf.nn.log_softmax(logits, -1)\n",
    "\n",
    "        row_indices= tf.range(len(actions))\n",
    "        indices = tf.transpose([row_indices, actions])\n",
    "        log_probs_for_actions = tf.gather_nd(log_probs, indices)\n",
    "        \n",
    "        #Compute loss to be minized\n",
    "        J = tf.reduce_mean(log_probs_for_actions*rewards_to_go)\n",
    "        H = -tf.reduce_mean(tf.reduce_sum(probs*log_probs, -1))\n",
    "\n",
    "        loss = -(J+entropy_coef*H)\n",
    "\n",
    "    grads = tape.gradient(loss, model.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
    "    \n",
    "    return np.sum(rewards) #to show progress on training\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean reward:23.162\n",
      "mean reward:35.212\n",
      "mean reward:46.111\n",
      "mean reward:67.444\n",
      "mean reward:133.162\n",
      "mean reward:168.242\n",
      "mean reward:195.071\n",
      "mean reward:236.697\n",
      "mean reward:293.697\n",
      "mean reward:223.859\n",
      "mean reward:205.889\n",
      "mean reward:287.444\n",
      "mean reward:529.778\n"
     ]
    }
   ],
   "source": [
    "total_rewards = []\n",
    "for i in range(10000):\n",
    "    states, actions, rewards = generate_trajectory(env)\n",
    "    reward = train_one_episode(states, actions, rewards)\n",
    "    total_rewards.append(reward)\n",
    "    if i != 0 and i % 100 == 0:\n",
    "        mean_reward = np.mean(total_rewards[-100:-1])\n",
    "        print(\"mean reward:%.3f\" % (mean_reward))\n",
    "        if mean_reward > 300:\n",
    "            break\n",
    "env.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let us record a video of trained agent**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_animation(env, save_dir):\n",
    "    try:\n",
    "        env = gym.wrappers.Monitor(\n",
    "            env, save_dir, video_callable=lambda id: True, force=True, mode='evaluation')\n",
    "    except gym.error.Error as e:\n",
    "        print(e)\n",
    "\n",
    "    if not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "        \n",
    "    generate_trajectory(env)\n",
    "\n",
    "\n",
    "\n",
    "def display_animation(filepath):\n",
    "    video = io.open(filepath, 'r+b').read()\n",
    "    encoded = base64.b64encode(video)\n",
    "    return HTML(data='''<video alt=\"test\" controls>\n",
    "                <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" />\n",
    "                 </video>'''.format(encoded.decode('ascii')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video alt=\"test\" controls>\n",
       "                <source src=\"data:video/mp4;base64,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type=\"video/mp4\" />\n",
       "                 </video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Animate learned policy\n",
    "save_dir='./videos/tensorflow/reinforce/'\n",
    "env = make_env(env_name)\n",
    "generate_animation(env, save_dir=save_dir)\n",
    "[filepath] = glob.glob(os.path.join(save_dir, '*.mp4'))\n",
    "display_animation(filepath)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
